Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Abstract

The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (1H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2–95.1%) and a specificity of 72.7% (95% CI, 57.2–85.0%) could be achieved. We concluded that our 1H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting.

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma

“…In general, the radiomic method is superior to the 2HG MRS analysis manifesting in heterogeneity assessment, availability, generalizability, and predictive modeling for glioma IDH genotyping ( 45 – 47 ). IDH mutant gliomas often exhibit significant intratumoral heterogeneity, with diverse regions of aggressiveness, therapy resistance, and molecular characteristics.…”

Artificial intelligence in cancer imaging for diagnosis and evaluation of treatment response

“…The inclusion of advanced MR techniques has improved the diagnostic performance of AI models. Bumes et al have applied magnetic resonance spectroscopy to build a machine learning model and achieved good diagnostic performance (sensitivity: 82.6%, specificity: 72.7%) [41]. Several advanced MRI techniques have also been applied to build AI models, such as DWI, ADC, T1 perfusion and ASL.…”

Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis

“…To date, no consensually approved method exists to determine IDH status in a noninvasive manner. Nevertheless, some studies using magnetic resonance imaging (MRI) radiomics [8] have shown high accuracy in predicting IDH status, with an area under the curve (AUC) > 0.9, using magnetic resonance (MR) spectroscopy with hydroxyglutarate, among other methods [9]. Conventional MRI represents the gold standard for the initial morphologic evaluation of a suspected brain tumor due to its high spatial resolution.…”